Introduction

Research Questions

What is STAG2? What should we expect if we knocked out STAG2 from a cell?

STAG2 is a important subunit in the cohesin complex that plays an important role in regulating sister chromatid alignment during cell division, and other genetic regulatory functions. Studies show that STAG2-mut EWS have higher rates of metastatic disease and worse outcomes. It is expected that the TC71 & A673 cell lines will have differing phenotype effects to STAG2 KO, for example STAG2 deletion will lead to TC71 growth defect but with A673 it will lead to a growth advantage. STAG1 levels could also increase, in order to possibly fill in for the removal of STAG2 in the cell cycle, a decrease of RAD21 is also observed. The cohesin complex also mediates intrachromosomal interactions including those conjoining enhancers to promoters. Loss of STAG2 produces highly consistent and stable transcriptional changes that may undergo selection to confer a competitive advantage. Two neurodvelopmental transcription factors, POU3F2 and NR2F1 were consistently upregulated in STAG2 KO studies. (Adane et al, 2022)

Data input:

All samples are downloaded from the SRA Run Selector BioProject PRJNA549593, slight quality control via fastp was conducted and then the reads were aligned using Salmon for Transcript-level quantification files. The metadata for the samples was provided from the SRA Run Selector also. 3 sample comparisons was conducted using 1. SA2 KO vs WT in A673 cells 2. SA2 KO vs WT in TC71 cells 3. SA2 KO in TC71 cells VS A673 cells.

samples <- read.table(file.path("Analysis/SraRunTable.txt"), sep = ",", header = TRUE) %>%
  dplyr::mutate(cell_line = ifelse(grepl("A673", x = source_name), "A673", "TC71")) %>%
  dplyr::mutate(condition = ifelse(grepl("WT|siCT", x = GENOTYPE), "Control", "Treatment"))

A673_samples <- samples %>%
  dplyr::filter(GENOTYPE %in% c("WT", "SA2 KO") & cell_line == "A673")
  
TC71_samples <- samples %>%
  dplyr::filter(GENOTYPE %in% c("SA2 KO", "WT") & cell_line == "TC71")

SA2KO_samples <- samples %>%
  dplyr::filter(GENOTYPE == "SA2 KO")

A673_salmon_files <- file.path("Salmon/salmon.out", A673_samples$Run, "quant.sf") %>%
  setNames(object = , A673_samples$Run)

TC71_salmon_files <- file.path("Salmon/salmon.out", TC71_samples$Run, "quant.sf") %>%
  setNames(object = , TC71_samples$Run)

SA2KO_salmon_files <- file.path("Salmon/salmon.out", SA2KO_samples$Run, "quant.sf") %>%
  setNames(object = , SA2KO_samples$Run)

ensdb <- EnsDb.Hsapiens.v86

transcripts <- transcripts(ensdb, columns = c(listColumns(ensdb, "tx"), "gene_name"),
  return.type = "data.frame") %>%
  as_tibble() %>%
  dplyr::select(tx_id, gene_name) 

A673_txi <- tximport(A673_salmon_files, type = "salmon", tx2gene = transcripts, ignoreTxVersion = TRUE)

TC71_txi <- tximport(TC71_salmon_files, type = "salmon", tx2gene = transcripts, ignoreTxVersion = TRUE)

SA2KO_txi <- tximport(SA2KO_salmon_files, type = "salmon", tx2gene = transcripts, ignoreTxVersion = TRUE)

A673_dds_txi <- DESeqDataSetFromTximport(A673_txi, colData = A673_samples, design = ~condition)

TC71_dds_txi <- DESeqDataSetFromTximport(TC71_txi, colData = TC71_samples, design = ~condition)

SA2KO_dds_txi <- DESeqDataSetFromTximport(SA2KO_txi, colData = SA2KO_samples, design = ~cell_line)

Exploratory Data

A673 PCA Plot

vst <- vst(A673_dds_txi)

A673_PCA <- plotPCA(vst, intgroup = c("cell_line", "GENOTYPE"), returnData = TRUE)
A673_percentVar <- round(100 * attr(A673_PCA, "percentVar"))

ggplot(A673_PCA, aes(PC1, PC2, color = GENOTYPE)) +
  geom_point(size = 3) +
  ggtitle("PCA Plot for A673 STAG2KO samples" ) +
  xlab(paste0("PC1: ", A673_percentVar[1], "% variance")) +
  ylab(paste0("PC2: ", A673_percentVar[2], "% variance")) +
  coord_fixed()

TC71 PCA Plot

TC71_vst <- vst(TC71_dds_txi)

TC71_PCA <- plotPCA(TC71_vst, intgroup = c("cell_line", "GENOTYPE"), returnData = TRUE)
TC71_percentVar <- round(100 * attr(TC71_PCA, "percentVar"))

ggplot(TC71_PCA, aes(PC1, PC2, color = GENOTYPE)) +
  geom_point(size = 3) +
  ggtitle("PCA Plot for TC71 STAG2KO samples" ) +
  xlab(paste0("PC1: ", A673_percentVar[1], "% variance")) +
  ylab(paste0("PC2: ", A673_percentVar[2], "% variance")) +
  coord_fixed()

All STAG2KO Samples PCA Plot

SA2KO_vst <- vst(SA2KO_dds_txi)

SA2KO_PCA <- plotPCA(SA2KO_vst, intgroup = c("cell_line", "GENOTYPE"), returnData = TRUE)
SA2KO_percentVar <- round(100 * attr(SA2KO_PCA, "percentVar"))

ggplot(SA2KO_PCA, aes(PC1, PC2, color = cell_line)) +
  geom_point(size = 3) +
  ggtitle("PCA Plot for All STAG2KO samples" ) +
  xlab(paste0("PC1: ", SA2KO_percentVar[1], "% variance")) +
  ylab(paste0("PC2: ", SA2KO_percentVar[2], "% variance")) +
  coord_fixed()

Sample Metadata

A673_metadata <- colData(A673_dds_txi)

TC71_metadata <- colData(TC71_dds_txi)

all_metadata <- rbind(A673_metadata, TC71_metadata)

all_metadata %>% 
  as.data.frame() %>%
  dplyr::select(GENOTYPE, cell_line) %>%
  kbl(caption = "Table 1: Sample Overview") %>%
  kable_styling(bootstrap_options = "striped", full_width = T, html_font = "Cambria")
Table 1: Sample Overview
GENOTYPE cell_line
SRR9326185 SA2 KO A673
SRR9326186 SA2 KO A673
SRR9326187 SA2 KO A673
SRR9326195 WT A673
SRR9326196 WT A673
SRR9326197 WT A673
SRR9326198 SA2 KO TC71
SRR9326199 SA2 KO TC71
SRR9326200 SA2 KO TC71
SRR9326201 SA2 KO TC71
SRR9326202 SA2 KO TC71
SRR9326203 SA2 KO TC71
SRR9326208 WT TC71
SRR9326209 WT TC71
SRR9326210 WT TC71

Results

Statistical Analysis

A673 Volcano Plot

EnhancedVolcano(A673_sig_ordered_result, 
                lab = A673_sig_ordered_result$Gene_name, 
                x = "log2FoldChange", 
                y = "pvalue", 
                title = "Siginifcant Genes for STAG2KO in A673 cells",
                subtitle = "",
                pointSize = 1.0, 
                labSize = 4.0,
                xlim = c(min(A673_sig_ordered_result$log2FoldChange), max(A673_sig_ordered_result$log2FoldChange)),
                ylim = c(0, 300)
                )

TC71 Volcano Plot

EnhancedVolcano(TC71_sig_ordered_result, 
                lab = TC71_sig_ordered_result$Gene_name, 
                x = "log2FoldChange", 
                y = "pvalue", 
                title =  "Siginifcant Genes for STAG2KO in TC71 Cells versus Control",
                subtitle = "",
                pointSize = 1.0, 
                labSize = 4.0,
                xlim = c(min(TC71_sig_ordered_result$log2FoldChange), max(TC71_sig_ordered_result$log2FoldChange)),
                ylim = c(0, 200)
                )

all STAG2KO samples Volcano Plot

EnhancedVolcano(SA2KO_sig_ordered_result, 
                lab = SA2KO_sig_ordered_result$Gene_name, 
                x = "log2FoldChange", 
                y = "pvalue", 
                title =  "Siginifcant Genes for between the STAG2KO samples in A673 and TC71 cell lines",
                subtitle = "",
                pointSize = 1.0, 
                labSize = 4.0,
                xlim = c(min(SA2KO_sig_ordered_result$log2FoldChange), max(SA2KO_sig_ordered_result$log2FoldChange)),
                ylim = c(0, 200)
                )

A672 Significant Genes Table

A673_table_result <- dplyr::select(A673_sig_ordered_result, Gene_name, log2FoldChange, stat, pvalue, padj)

datatable(A673_table_result, class = 'cell-border stripe', 
          caption = "Table 2: A672 STAG2KO Differentally Significant Genes")

TC71 Significant Genes Table

TC71_table_result <- dplyr::select(TC71_sig_ordered_result, Gene_name, log2FoldChange, stat, pvalue, padj)

datatable(TC71_table_result, class = 'cell-border stripe', 
          caption = "Table 3: TC71 STAG2KO Differentally Significant Genes")

all STAG2KO samples Significant Genes Table

SA2KO_table_result <- dplyr::select(SA2KO_sig_ordered_result, Gene_name, log2FoldChange, stat, pvalue, padj)

datatable(SA2KO_table_result, class = 'cell-border stripe', 
          caption = "Table 4: STAG2KO samples Differentally Significant Genes")

Top 10 Overexpressed and 10 Underexpressed genes for STAG2KO in A673 cells

A673_heatmap <- pheatmap(A673_sig_norm_dds_counts,
                    main = "Top 10 Over- and Under- expressed A673 STAG2KO DEGs",
                    color = palette(200), 
                    cluster_rows = FALSE,
                    cluster_cols = FALSE,
                    show_rownames = TRUE, 
                    annotation = dplyr::select(A673_heat_meta, condition), 
                    scale = "row")

Top 10 Overexpressed and 10 Underexpressed genes for STAG2KO in A673 cells

TC71_heatmap <- pheatmap(TC71_sig_norm_dds_counts,
                    main = "Top 10 Over- and Under- expressed TC71 STAG2KO DEGs",
                    color = palette(200), 
                    cluster_rows = FALSE,
                    cluster_cols = FALSE,
                    show_rownames = TRUE, 
                    annotation = dplyr::select(TC71_heat_meta, condition), 
                    scale = "row")

Top 10 Overexpressed and 10 Underexpressed genes for STAG2KO in A673 cells

SA2KO_heatmap <- pheatmap(SA2KO_sig_norm_dds_counts,
                    main = "Top 10 Over- and Under- expressed STAG2KO sample DEGs",
                    color = palette(200), 
                    cluster_rows = FALSE,
                    cluster_cols = FALSE,
                    show_rownames = TRUE, 
                    annotation = dplyr::select(SA2KO_heat_meta, cell_line), 
                    scale = "row")

GSEA Enrichment analysis for A673

A673_ordered_gse <- A673_gse %>%
  dplyr::arrange(desc(abs(NES)))

A673_ordered_gse_df <- A673_ordered_gse %>%
  as_tibble() %>%
  dplyr::select(ONTOLOGY, ID, Description, NES, pvalue, p.adjust, qvalue)

datatable(A673_ordered_gse_df, class = 'cell-border stripe', 
          caption = "Table 5: GSEA enrichment results for A673 STAG2KO significant DEGs")

Top Ranked enrichched pathway for A673 STAG2KO vs Control

gseaplot2(A673_ordered_gse, geneSetID = 1, title = A673_ordered_gse$Description[1])

GSEA Enrichment analysis for TC71

TC71_ordered_gse <- TC71_gse %>%
  dplyr::arrange(desc(abs(NES)))

TC71_ordered_gse_df <- TC71_ordered_gse %>%
  as_tibble() %>%
  dplyr::select(ONTOLOGY, ID, Description, NES, pvalue, p.adjust, qvalue)

datatable(TC71_ordered_gse_df, class = 'cell-border stripe', 
          caption = "Table 6: GSEA enrichment results for TC71 STAG2KO significant DEGs")

Top Ranked enrichched pathway for TC71 STAG2KO vs Control

gseaplot2(TC71_ordered_gse, geneSetID = 1, title = TC71_ordered_gse$Description[1])

GSEA Enrichment analysis for all STAG2KO samples

SA2KO_ordered_gse <- SA2KO_gse %>%
  dplyr::arrange(desc(abs(NES)))

SA2KO_ordered_gse_df <- SA2KO_ordered_gse %>%
  as_tibble() %>%
  dplyr::select(ONTOLOGY, ID, Description, NES, pvalue, p.adjust, qvalue)

datatable(SA2KO_ordered_gse_df, class = 'cell-border stripe', 
          caption = "Table 7: GSEA enrichment results for all STAG2KO samples significant DEGs")

Top Ranked enrichched pathway for all STAG2KO samples

gseaplot2(SA2KO_ordered_gse, geneSetID = 1, title = SA2KO_ordered_gse$Description[1])

A673 enrichr Pathway enrichment

A673_resRmd <- llply(names(A673_gene_list), function(groupNow) {
  genesNow <- A673_gene_list[[groupNow]]
  response <- httr::POST(  
    url = 'https://maayanlab.cloud/Enrichr/addList', 
    body = list(
      'list' = paste0(genesNow, collapse = "\n"),
      'description' = groupNow
      )
    )
  response <- jsonlite::fromJSON(httr::content(response, as = "text"))  
  permalink <- paste0("https://maayanlab.cloud/Enrichr/enrich?dataset=", 
                      response$shortId[1])
  knitr::knit_child(text = c( 
    '### `r groupNow`',
    '',
    'Enrichr Link: <a href="`r permalink`" target="_blank">`r groupNow`</a>.',
    ''
  ), 
  envir = environment(),  
  quiet = TRUE)
})
cat(unlist(A673_resRmd), sep = '\n')

Over-expressed

Enrichr Link: Over-expressed.

Under-expressed

Enrichr Link: Under-expressed.

TC71 enrichr Pathway enrichment

TC71_resRmd <- llply(names(TC71_gene_list), function(groupNow) {
  genesNow <- TC71_gene_list[[groupNow]]
  response <- httr::POST(  
    url = 'https://maayanlab.cloud/Enrichr/addList', 
    body = list(
      'list' = paste0(genesNow, collapse = "\n"),
      'description' = groupNow
      )
    )
  response <- jsonlite::fromJSON(httr::content(response, as = "text"))  
  permalink <- paste0("https://maayanlab.cloud/Enrichr/enrich?dataset=", 
                      response$shortId[1])
  knitr::knit_child(text = c( 
    '### `r groupNow`',
    '',
    'Enrichr Link: <a href="`r permalink`" target="_blank">`r groupNow`</a>.',
    ''
  ), 
  envir = environment(),  
  quiet = TRUE)
})
cat(unlist(TC71_resRmd), sep = '\n')

Over-expressed

Enrichr Link: Over-expressed.

Under-expressed

Enrichr Link: Under-expressed.

all STAG2KO samples enrichr Pathway enrichment

SA2KO_resRmd <- llply(names(SA2KO_gene_list), function(groupNow) {
  genesNow <- SA2KO_gene_list[[groupNow]]
  response <- httr::POST(  
    url = 'https://maayanlab.cloud/Enrichr/addList', 
    body = list(
      'list' = paste0(genesNow, collapse = "\n"),
      'description' = groupNow
      )
    )
  response <- jsonlite::fromJSON(httr::content(response, as = "text"))  
  permalink <- paste0("https://maayanlab.cloud/Enrichr/enrich?dataset=", 
                      response$shortId[1])
  knitr::knit_child(text = c( 
    '### `r groupNow`',
    '',
    'Enrichr Link: <a href="`r permalink`" target="_blank">`r groupNow`</a>.',
    ''
  ), 
  envir = environment(),  
  quiet = TRUE)
})
cat(unlist(SA2KO_resRmd), sep = '\n')

Over-expressed

Enrichr Link: Over-expressed.

Under-expressed

Enrichr Link: Under-expressed.

Works Cited

Adane B, Alexe G, Seong BKA, Lu D, Hwang EE, Hnisz D, Lareau CA, Ross L, Lin S, Dela Cruz FS, Richardson M, Weintraub AS, Wang S, Iniguez AB, Dharia NV, Conway AS, Robichaud AL, Tanenbaum B, Krill-Burger JM, Vazquez F, Schenone M, Berman JN, Kung AL, Carr SA, Aryee MJ, Young RA, Crompton BD, Stegmaier K. STAG2 loss rewires oncogenic and developmental programs to promote metastasis in Ewing sarcoma. Cancer Cell. 2021 Jun 14;39(6):827-844.e10. doi: 10.1016/j.ccell.2021.05.007. PMID: 34129824; PMCID: PMC8378827.